View source for 20230106136. ELECTRONIC APPARATUS AND GENERATING METHOD OF TARGET DOMAIN simplified abstract (Samsung Electronics Co., Ltd.)

=ELECTRONIC APPARATUS AND GENERATING METHOD OF TARGET DOMAIN=


==Organization Name==

[[:Category:Samsung Electronics Co., Ltd.|Samsung Electronics Co., Ltd.]]


[[Category:Samsung Electronics Co., Ltd.]]

==Inventor(s)==

[[:Category:Sangyeon Kim of Suwon-si (KR)|Sangyeon Kim of Suwon-si (KR)]][[Category:Sangyeon Kim of Suwon-si (KR)]]

[[:Category:Hyunwoo Lee of Suwon-si (KR)|Hyunwoo Lee of Suwon-si (KR)]][[Category:Hyunwoo Lee of Suwon-si (KR)]]

[[:Category:Jonghee Han of Suwon-si (KR)|Jonghee Han of Suwon-si (KR)]][[Category:Jonghee Han of Suwon-si (KR)]]

==ELECTRONIC APPARATUS AND GENERATING METHOD OF TARGET DOMAIN - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20230106136 titled 'ELECTRONIC APPARATUS AND GENERATING METHOD OF TARGET DOMAIN

==Simplified Explanation==

The abstract describes an electronic apparatus that generates a target domain using a generative adversarial network (GAN) and a method for generating this target domain. The method involves reconstructing source data from a source domain, training the reconstructed source data to generate target data based on the source data, and creating a target domain that includes the generated target data. During the training process, the method identifies a loss value between a class loss value and a distance loss value, and applies this loss value to the reconstructed source data.

* The electronic apparatus generates a target domain using a generative adversarial network (GAN).
* The method involves reconstructing source data from a source domain.
* The reconstructed source data is trained to generate target data based on the source data.
* The generated target data is used to create a target domain.
* During training, the method identifies a loss value between a class loss value and a distance loss value.
* This loss value is applied to the reconstructed source data.

== Potential Applications ==
* Image generation and manipulation
* Data augmentation for machine learning models
* Synthetic data generation for training AI algorithms

== Problems Solved ==
* Lack of diverse and representative data for training machine learning models
* Difficulty in generating realistic and high-quality synthetic data
* Limited availability of labeled data for specific domains

== Benefits ==
* Enables the generation of realistic and diverse data in a target domain
* Improves the performance and generalization of machine learning models
* Reduces the reliance on large amounts of labeled data for training AI algorithms


==Original Abstract Submitted==

an electronic apparatus generating a generative adversarial network (gan)-based target domain and a generating method thereof is provided. the generating method includes reconstructing source data included in a source domain, generating target data by training the reconstructed source data based on the source data, and generating a target domain including the generated target data, and the training comprises identifying at least one loss value between a class loss value by class loss and a distance loss value by distance matrix loss and applying at least one loss value between the identified class loss value and the distance loss value to the reconstructed source data.



[[Category:G06N3/04]]
[[Category:G06N3/08]]